Model Transparency

A Model Transparency is a transparency practice that provides visibility into computational models, statistical models, or mathematical models including their model structure, model assumptions, model limitations, and model behavior.

• AKA: Model Openness, Model Interpretability, Model Explainability.
• Context:
  • It can typically reveal Model Architecture including model components, model parameters, and model relationships.
  • It can typically disclose Model Development Process covering model design choices, model training methods, and model validation techniques.
  • It can typically document Model Assumptions underlying model predictions, model inferences, and model recommendations.
  • It can typically communicate Model Performance Metrics through model accuracy measures, model error rates, and model confidence intervals.
  • It can typically expose Model Limitations including model boundary conditions, model failure modes, and model uncertainty sources.
  • ...
  • It can often enable Model Reproducibility through shared model specifications and model implementation details.
  • It can often facilitate Model Validation by external model auditors and domain experts.
  • It can often support Model Improvement through identified model weaknesses and enhancement opportunitys.
  • It can often promote Model Trust via demonstrated model reliability and model fairness.
  • ...
  • It can range from being a Black-Box Model Transparency to being a White-Box Model Transparency, depending on its model visibility level.
  • It can range from being a Static Model Transparency to being a Dynamic Model Transparency, depending on its model monitoring approach.
  • It can range from being a Technical Model Transparency to being a Accessible Model Transparency, depending on its model explanation complexity.
  • ...
  • It can be achieved through Model Documentation Standards structuring model information.
  • It can be enhanced via Model Visualization Tools illustrating model behavior.
  • It can be validated using Model Audit Frameworks verifying transparency claims.
  • It can be mandated by Model Governance Policy requiring transparency levels.
  • ...
• Example(s):
  • Statistical Model Transparencys, such as:
    • Regression Model Transparency showing coefficient values and significance tests.
    • Classification Model Transparency revealing decision boundarys and feature importance.
    • Time Series Model Transparency exposing seasonal patterns and trend components.
  • Financial Model Transparencys, such as:
    • Risk Model Transparency documenting risk factors and correlation assumptions.
    • Pricing Model Transparency revealing valuation methods and market assumptions.
    • Credit Model Transparency showing scoring algorithms and default predictions.
  • Scientific Model Transparencys, such as:
    • Climate Model Transparency disclosing physical assumptions and uncertainty ranges.
    • Epidemiological Model Transparency revealing transmission parameters and intervention effects.
    • AI Model Transparency exposing AI architectures and AI training processes.
  • ...
• Counter-Example(s):
  • Model Secrecy, which conceals model details for competitive or security reasons.
  • Model Output Disclosure, which shares results without revealing model internals.
  • Model Marketing Description, which simplifies without providing technical transparency.
  • Proprietary Model, which restricts access to model information.
• See: Transparency Practice, Model Documentation, Model Interpretability, Model Governance, Computational Transparency.

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